Databricks-Certified-Data-Engineer-Associate Practice Exam Questions with Answers Databricks Certified Data Engineer Associate Exam Certification

 Option A is the correct answer because it uses the FILTER higher-order function correctly to filter out employees with more than 5 years of experience from the array column “employees”. It applies a lambda function i -> i.years_exp > 5 that checks if the years of experience of each employee in the array is greater than 5. If this condition is met, the employee is included in the new array column “exp_employees”.

 In Databricks SQL, you can use scheduled query executions to update your dashboards or enable routine alerts. By default, your queries do not have a schedule. To set the schedule, you can use the dropdown pickers to specify the frequency, period, starting time, and time zone. You can also choose to end the schedule on a certain date by selecting the End date checkbox and picking a date from the calendar. This way, you can ensure that the query does not run beyond the first week of the project’s release and does not incur any additional cost. Option A is incorrect, as setting a limit to the number of DBUs does not stop the query from running. Option B is incorrect, as there is no option to end the schedule after a certain number of refreshes. Option C is incorrect, as there is a way to ensure the query does not cost the organization money beyond the first week of the project’s release. Option D is incorrect, as setting a limit to the number of individuals who can manage the query’s refresh schedule does not affect the query’s execution or cost. References: Schedule a query, Schedule a query - Azure Databricks - Databricks SQL

Databricks Repos is a visual Git client and API in Databricks that supports common Git operations such as commit, pull, push, branch management, and visual comparison of diffs when committing1. However, merge is not supported in the Git dialog2. You need to use the Repos UI or your Git provider to merge branches3. Merge is a way to combine the commit history from one branch into another branch1. During a merge, a merge conflict is encountered when Git cannot automatically combine code from one branch into another. Merge conflicts require manual resolution before a merge can be completed1. References: 4: Run Git operations on Databricks Repos4, 1: CI/CD techniques with Git and Databricks Repos1, 3: Collaborate in Repos3, 2: Databricks Repos - What it is and how we can use it2.

Databricks Repos is a visual Git client and API in Databricks that supports common Git operations such as commit, pull, push, merge, and branch management. However, to clone a remote Git repository to a Databricks repo, you must use the Databricks UI or API. You cannot clone a Git repo using the CLI through a cluster’s web terminal, as the files won’t display in the Databricks UI1. References: 1: Run Git operations on Databricks Repos | Databricks on AWS2

 Delta Live Tables expectations are optional clauses that apply data quality checks on each record passing through a query. An expectation consists of a description, a boolean statement, and an action to take when a record fails the expectation. The ON VIOLATION clause specifies the action to take, which can be one of the following: warn, drop, or fail. The drop action means that invalid records are dropped from the target dataset before data is written to the target. The failure is reported as a metric for the dataset, which can be viewed by querying the Delta Live Tables event log. The event log contains information such as the number of records that violate an expectation, the number of records dropped, and the number of records written to the target dataset. References:

Manage data quality with Delta Live Tables

Monitor Delta Live Tables pipelines

Delta Live Tables SQL language reference

The expected behavior when a batch of data containing data that violates the expectation is processed is that the job will fail. This is because the expectation clause has the ON VIOLATION FAIL UPDATE option, which means that if any record in the batch does not meet the expectation, the entire batch will be rejected and the job will fail. This option is useful for enforcing strict data quality rules and preventing invalid data from entering the target dataset.

Option A is not correct, as the ON VIOLATION FAIL UPDATE option does not drop the records that violate the expectation, but fails the entire batch. To drop the records that violate the expectation and record them as invalid in the event log, the ON VIOLATION DROP RECORD option should be used.

Option C is not correct, as the ON VIOLATION FAIL UPDATE option does not drop the records that violate the expectation, but fails the entire batch. To drop the records that violate the expectation and load them into a quarantine table, the ON VIOLATION QUARANTINE RECORD option should be used.

Option D is not correct, as the ON VIOLATION FAIL UPDATE option does not add the records that violate the expectation, but fails the entire batch. To add the records that violate the expectation and record them as invalid in the event log, the ON VIOLATION LOG RECORD option should be used.

Option E is not correct, as the ON VIOLATION FAIL UPDATE option does not add the records that violate the expectation, but fails the entire batch. To add the records that violate the expectation and flag them as invalid in a field added to the target dataset, the ON VIOLATION FLAG RECORD option should be used.

 In a medallion architecture, a common data design pattern for lakehouses, data flows from Bronze to Silver to Gold layer tables, with each layer progressively improving the structure and quality of data. Bronze tables store raw data ingested from various sources, while Silver tables apply minimal transformations and cleansing to create an enterprise view of the data. Silver tables can also join and enrich data from different Bronze tables to provide a more complete and consistent view of the data. Therefore, option D is the correct answer, as Silver tables contain a more refined and cleaner view of data than Bronze tables. Option A is incorrect, as it is the opposite of the correct answer. Option B is incorrect, as Silver tables do not necessarily contain aggregates, but can also store detailed records. Option C is incorrect, as Silver tables may contain less data than Bronze tables, depending on the transformations and cleansing applied. Option E is incorrect, as Silver tables may contain more data than Bronze tables, depending on the joins and enrichments applied. References: What is a Medallion Architecture?, Transforming Bronze Tables in Silver Tables, What is the medallion lakehouse architecture?

The data engineering team can use the spark.table method to access the Delta table sales in PySpark. This method returns a DataFrame representation of the Delta table, which can be used for further processing or testing. The spark.table method works for any table that is registered in the Hive metastore or the Spark catalog, regardless of the file format1. Alternatively, the data engineering team can also use the DeltaTable.forPath method to load the Delta table from its path2. References: 1: SparkSession | PySpark 3.2.0 documentation 2: Welcome to Delta Lake’s Python documentation page — delta-spark 2.4.0 documentation

 A data lakehouse is a new paradigm that can be used to simplify and unify siloed data architectures that are specialized for specific use cases. A data lakehouse combines the best of both data lakes and data warehouses, providing a single platform that supports diverse data types, open standards, low-cost storage, high-performance queries, ACID transactions, schema enforcement, and governance. A data lakehouse enables data engineers to build reliable and scalable data pipelines that can serve various downstream applications and users, such as data science, machine learning, analytics, and reporting. A data lakehouse leverages the power of Delta Lake, a storage layer that brings reliability and performance to data lakes. References: What is a data lakehouse?, Delta Lake, Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics

Auto Loader is a Structured Streaming source that incrementally and efficiently processes new data files as they arrive in cloud storage. It supports both Python and SQL in Delta Live Tables, which are ideal for building streaming data pipelines. Auto Loader can handle near real-time ingestion of millions of files per hour and provide exactly-once guarantees when writing data into Delta Lake. Auto Loader is not designed for dashboard, machine learning, serverless, or batch workloads, which have different requirements and characteristics. References: What is Auto Loader?, Delta Live Tables

Auto Loader in Databricks utilizes Spark Structured Streaming for processing data incrementally. This allows Auto Loader to efficiently ingest streaming or batch data at scale and to recognize new data as it arrives in cloud storage. Spark Structured Streaming provides the underlying engine that supports various incremental data loading capabilities like schema inference and file notification mode, which are crucial for the dynamic nature of data lakes.

To read from a stream source, the data engineer needs to use the spark.readStream method instead of the spark.read method. The spark.readStream method returns a DataStreamReader object that can be used to specify the details of the input source, such as the format, the schema, the path, and the options. The spark.read method is only suitable for batch processing, not streaming processing. The other changes are not necessary or correct for reading from a stream source. References: Structured Streaming Programming Guide, Read a stream, Databricks Data Sources

https://www.w3schools.com/python/python_functions.asp

https://www.geeksforgeeks.org/python-functions/

 A data lakehouse is a data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data12. By using a data lakehouse, both the data analysis and data engineering teams can access the same data sources and formats, ensuring data consistency and quality across their reports. A data lakehouse also supports schema enforcement and evolution, data validation, and time travel to old table versions, which can help resolve data conflicts and errors1. References: 1: What is a Data Lakehouse? - Databricks 2: What is a data lakehouse? | IBM

Delta Live Tables is a tool that enables data engineers to build and manage reliable data pipelines with minimal code. One of the features of Delta Live Tables is data quality monitoring, which allows data engineers to define quality expectations for their data and automatically check them at every step of the pipeline. Data quality monitoring can help detect and resolve data quality issues, such as missing values, duplicates, outliers, or schema changes. Data quality monitoring can also generate alerts and reports on the quality level of the data, and enable data engineers to troubleshoot and fix problems quickly. References: Delta Live Tables Overview, Data Quality Monitoring

The Databricks web application is the user interface that allows you to create and manage workspaces, clusters, notebooks, jobs, and other resources. It is hosted completely in the control plane of the classic Databricks architecture, which includes the backend services that Databricks manages in your Databricks account. The other options are part of the compute plane, which is where your data is processed by compute resources such as clusters. Thecompute plane is in your own cloud account and network. References: Databricks architecture overview, Security and Trust CenterQUESTION NO: 4

Which of the following benefits of using the Databricks Lakehouse Platform is provided by Delta Lake?

A. The ability to manipulate the same data using a variety of languages

B. The ability to collaborate in real time on a single notebook

C. The ability to set up alerts for query failures

D. The ability to support batch and streaming workloads

E. The ability to distribute complex data operations

Answer: D

Delta Lake is the optimized storage layer that provides the foundation for storing data and tables in the Databricks lakehouse. Delta Lake is fully compatible with Apache Spark APIs, and was developed for tight integration with Structured Streaming, allowing you to easily use a single copy of data for both batch and streaming operations and providing incremental processing at scale1. Delta Lake supports upserts using the merge operation, which enables you to efficiently update existing data or insert new data into your Delta tables2. Delta Lake also provides time travel capabilities, which allow you to query previous versions of your data or roll back to a specific point in time3. References: 1: What is Delta Lake? | Databricks on AWS 2: Upsert into a table using merge | Databricks on AWS 3: [Query an older snapshot of a table (time travel) | Databricks on AWS]

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The MERGE command can be used to upsert data from a source table, view, or DataFrame into a target Delta table. It allows you to specify conditions for matching and updating existing records, and inserting new records when no match is found. This way, you can avoid writing duplicate records into a Delta table1. The other commands (DROP, IGNORE, APPEND, INSERT) do not have this functionality and may result in duplicate records or data loss234. References: 1: Upsert into a Delta Lake table using merge | Databricks on AWS 2: SQL DELETE | Databricks on AWS 3: SQL INSERT INTO | Databricks on AWS 4: SQL UPDATE | Databricks on AWS

https://community.databricks.com/t5/data-engineering/sequential-vs-concurrency-optimization-questions-from-query/td-p/36696

A data engineer can create a multi-task job in Databricks that consists of multiple tasks that run in a specific order. Each task can have one or more dependencies, which are other tasks that must run before the current task. The Depends On field of a new Databricks Job Task allows the data engineer to specify the dependencies of the task. The data engineer should select a task in the Depends On field when they want the new task to run only after the selected task has successfully completed. This can help the data engineer to create a logical sequence of tasks that depend on each other’s outputs or results. For example, a data engineer can create a multi-task job that consists of the following tasks:

Task A: Ingest data from a source using Auto Loader

Task B: Transform the data using Spark SQL

Task C: Write the data to a Delta Lake table

Task D: Analyze the data using Spark ML

Task E: Visualize the data using Databricks SQL

In this case, the data engineer can set the dependencies of each task as follows:

Task A: No dependencies

Task B: Depends on Task A

Task C: Depends on Task B

Task D: Depends on Task C

Task E: Depends on Task D

This way, the data engineer can ensure that each task runs only after the previous task has successfully completed, and the data flows smoothly from ingestion to visualization.

The other options are incorrect because they do not describe valid scenarios for selecting a task in the Depends On field. The Depends On field does not affect the following aspects of a task:

Whether the task needs to be replaced by another task

Whether the task needs to fail before another task begins

Whether the task has the same dependency libraries as another task

Whether the task needs to use as little compute resources as possible References: Create a multi-task job, Run tasks conditionally in a Databricks job, Databricks Jobs.

The processingTime option specifies a time-based trigger interval for fixed interval micro-batches. This means that the query will execute a micro-batch to process data every 5 seconds, regardless of how much data is available. This option is suitable for near-real time processing workloads that require low latency and consistent processing frequency. The other options are either invalid syntax (A, C), default behavior (B), or experimental feature (E). References: Databricks Documentation - Configure Structured Streaming trigger intervals, Databricks Documentation - Trigger.

One of the benefits of the Databricks Lakehouse Platform embracing open source technologies is that it avoids vendor lock-in. This means that customers can use the same open source tools and frameworks across different cloud providers, and migrate their data and workloads without being tied to a specific vendor. The Databricks Lakehouse Platform is built on open source projects such as Apache Spark™, Delta Lake, MLflow, and Redash, which are widely used and trusted by millions of developers. By supporting these open source technologies, the Databricks Lakehouse Platform enables customers to leverage the innovation and community of the open source ecosystem, and avoid the risk of being locked into proprietary or closed solutions. Theother options are either not related to open source technologies (A, B, C, D), or not benefits of the Databricks Lakehouse Platform (A, B). References: Databricks Documentation - Built on open source, Databricks Documentation - What is the Lakehouse Platform?, Databricks Blog - Introducing the Databricks Lakehouse Platform.

The SQL keyword that can be used to convert a table from a long format to a wide format is PIVOT. The PIVOT clause is used to rotate the rows of a table into columns of a new table1. The PIVOT clause can aggregate the values of a column based on the distinct values of another column, and use those values as the column names of the new table1. The PIVOT clause can be useful for transforming data from a long format, where each row represents an observation with multiple attributes, to a wide format, where each row represents an observation with a single attribute and multiple values2. For example, the PIVOT clause can be used to convert a table that contains the sales of different products by different regions into a table that contains the sales of each product by each region as separate columns1.

The other options are not suitable for converting a table from a long format to a wide format. CONVERT is a function that can be used to change the data type of an expression3. WHERE is a clause that can be used to filter the rows of a table based on a condition4. TRANSFORM is a keyword that can be used to apply a user-defined function to a group of rows in a table5. SUM is a function that can be used to calculate the total of a numeric column.

The correct command to create a new table that contains all records from two tables without duplicate records is to use the UNION operator. The UNION operator combines the results of two queries and removes any duplicate rows. The INNER JOIN, OUTER JOIN, and MERGE operators do not remove duplicate rows, and the INTERSECT operator only returns the rows that are common to both tables. Therefore, option B is the only correct answer. References: Databricks SQL Reference - UNION, Databricks SQL Reference - JOIN, Databricks SQL Reference - MERGE, [Databricks SQL Reference - INTERSECT]